{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一期 Pandas基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.将下面的字典创建为DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"grammer\":[\"Python\",\"C\",\"Java\",\"GO\",\"R\",\"SQL\",\"PHP\",\"Python\"],\n",
    "       \"score\":[1,2,np.nan,4,5,6,7,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  score\n",
       "0  Python    1.0\n",
       "1       C    2.0\n",
       "2    Java    NaN\n",
       "3      GO    4.0\n",
       "4       R    5.0\n",
       "5     SQL    6.0\n",
       "6     PHP    7.0\n",
       "7  Python   10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.提取含有字符串\"Python\"的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  grammer  score\n",
      "0  Python    1.0\n",
      "7  Python   10.0\n"
     ]
    }
   ],
   "source": [
    "result=df[df['grammer'].str.contains(\"Python\")]\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.输出df的所有列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['grammer', 'score'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.修改第二列列名为'popularity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         NaN\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'score':'popularity'}, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.统计grammer列中每种编程语言出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    2\n",
       "R         1\n",
       "Java      1\n",
       "C         1\n",
       "SQL       1\n",
       "GO        1\n",
       "PHP       1\n",
       "Name: grammer, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grammer'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.将空值用上下值的平均值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         3.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'] = df['popularity'].fillna(df['popularity'].interpolate())\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.提取popularity列中值大于3的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Python</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0\n",
       "7  Python        10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['popularity'] > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.按照grammer列进行去除重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PHP</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "0  Python         1.0\n",
       "1       C         2.0\n",
       "2    Java         3.0\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0\n",
       "6     PHP         7.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(['grammer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.计算popularity列平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.75"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.将grammer列转换为list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Python', 'C', 'Java', 'GO', 'R', 'SQL', 'PHP', 'Python']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grammer'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.将DataFrame保存为EXCEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('test.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.查看数据行列数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.提取popularity列值大于3小于7的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grammer</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SQL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  grammer  popularity\n",
       "3      GO         4.0\n",
       "4       R         5.0\n",
       "5     SQL         6.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['popularity'] > 3) & (df['popularity'] < 7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14.交换两列位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n方法2\\ncols = df.columns[[1,0]]\\ndf = df[cols]\\ndf\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "方法1\n",
    "'''\n",
    "temp = df['popularity']\n",
    "df.drop(labels=['popularity'], axis=1,inplace = True)\n",
    "df.insert(0, 'popularity', temp)\n",
    "df\n",
    "'''\n",
    "方法2\n",
    "cols = df.columns[[1,0]]\n",
    "df = df[cols]\n",
    "df\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15.提取popularity列最大值所在行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df['popularity']\n",
    "df.drop(labels=['popularity'], axis=1,inplace = True)\n",
    "df.insert(0, 'popularity', temp)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16.查看最后5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7        10.0  Python"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17.删除最后一行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([len(df)-1],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18.添加一行数据['Perl',6.6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Perl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "6         7.0     PHP\n",
       "7         6.6    Perl"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row={'grammer':'Perl','popularity':6.6}\n",
    "df = df.append(row,ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19.对数据按照\"popularity\"列值的大小进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>grammer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SQL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.6</td>\n",
       "      <td>Perl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>PHP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity grammer\n",
       "0         1.0  Python\n",
       "1         2.0       C\n",
       "2         3.0    Java\n",
       "3         4.0      GO\n",
       "4         5.0       R\n",
       "5         6.0     SQL\n",
       "7         6.6    Perl\n",
       "6         7.0     PHP"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"popularity\",inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20.统计grammer列每个字符串的长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    1\n",
       "2    4\n",
       "3    2\n",
       "4    1\n",
       "5    3\n",
       "7    4\n",
       "6    3\n",
       "Name: grammer, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#方法1\n",
    "df['grammer'].map(lambda x: len(x))\n",
    "#方法二\n",
    "#df.grammer.str.len()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二期 Pandas数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21.读取本地EXCEL数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('pandas120.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 22.查看df数据前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>20k-40k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>20k-35k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>13k-20k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>10k-20k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime education   salary\n",
       "0 2020-03-16 11:30:18        本科  20k-35k\n",
       "1 2020-03-16 10:58:48        本科  20k-40k\n",
       "2 2020-03-16 10:46:39        不限  20k-35k\n",
       "3 2020-03-16 10:45:44        本科  13k-20k\n",
       "4 2020-03-16 10:20:41        本科  10k-20k"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 23.将salary列数据转换为最大值与最小值的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-16 11:30:18</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-16 10:58:48</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-16 10:46:39</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-16 10:45:44</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-16 10:20:41</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2020-03-16 11:36:07</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2020-03-16 09:54:47</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2020-03-16 10:48:32</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2020-03-16 10:46:31</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020-03-16 11:19:38</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             createTime education salary\n",
       "0   2020-03-16 11:30:18        本科  27500\n",
       "1   2020-03-16 10:58:48        本科  30000\n",
       "2   2020-03-16 10:46:39        不限  27500\n",
       "3   2020-03-16 10:45:44        本科  16500\n",
       "4   2020-03-16 10:20:41        本科  15000\n",
       "..                  ...       ...    ...\n",
       "130 2020-03-16 11:36:07        本科  14000\n",
       "131 2020-03-16 09:54:47        硕士  37500\n",
       "132 2020-03-16 10:48:32        本科  30000\n",
       "133 2020-03-16 10:46:31        本科  19000\n",
       "134 2020-03-16 11:19:38        本科  30000\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#为什么不能直接使用max，min函数，因为我们的数据中是20k-35k这种字符串，所以需要先用正则表达式提取数字\n",
    "import re\n",
    "for i in range(len(df)):\n",
    "    str1 = df.iloc[i,2]\n",
    "    # re.findall(pattern, str), 将str中符合pattern的字符串，存储到列表中，返回的是一个列表\n",
    "    k = re.findall(r\"\\d+\\.?\\d*\",str1)  # ？匹配前一个字符出现0次或多次\n",
    "    salary = ((int(k[0]) + int(k[1]))/2)*1000\n",
    "    df.iloc[i,2] = salary\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24.将数据根据学历进行分组并计算最高薪资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "不限    30000.0\n",
       "大专    15000.0\n",
       "本科    45000.0\n",
       "硕士    37500.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('education')[\"salary\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 25.将createTime列时间转换为月-日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  createTime education salary\n",
       "0      03-16        本科  27500\n",
       "1      03-16        本科  30000\n",
       "2      03-16        不限  27500\n",
       "3      03-16        本科  16500\n",
       "4      03-16        本科  15000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for i in range(len(df)):\n",
    "    df.iloc[i,0] = df.iloc[i,0].to_pydatetime().strftime(\"%m-%d\")  \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 26.查看索引、数据类型和内存信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135 entries, 0 to 134\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   createTime  135 non-null    object\n",
      " 1   education   135 non-null    object\n",
      " 2   salary      135 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 27.查看数值型列的汇总统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>133</td>\n",
       "      <td>119</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       createTime education   salary\n",
       "count         135       135    135.0\n",
       "unique          3         4     36.0\n",
       "top         03-16        本科  30000.0\n",
       "freq          133       119     19.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 28.新增一列根据salary将数据分为三组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary categories\n",
       "0        03-16        本科  27500          高\n",
       "1        03-16        本科  30000          高\n",
       "2        03-16        不限  27500          高\n",
       "3        03-16        本科  16500          中\n",
       "4        03-16        本科  15000          中\n",
       "..         ...       ...    ...        ...\n",
       "130      03-16        本科  14000          中\n",
       "131      03-16        硕士  37500          高\n",
       "132      03-16        本科  30000          高\n",
       "133      03-16        本科  19000          中\n",
       "134      03-16        本科  30000          高\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0,5000, 20000, 50000]\n",
    "group_names = ['低', '中', '高']\n",
    "df['categories'] = pd.cut(df['salary'], bins, labels=group_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 29.按照salary列对数据降序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>45000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>40000</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4000</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>4000</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>3500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>3500</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary categories\n",
       "53       03-16        本科  45000          高\n",
       "37       03-16        本科  40000          高\n",
       "101      03-16        本科  37500          高\n",
       "16       03-16        本科  37500          高\n",
       "131      03-16        硕士  37500          高\n",
       "..         ...       ...    ...        ...\n",
       "123      03-16        本科   4500          低\n",
       "126      03-16        本科   4000          低\n",
       "110      03-16        本科   4000          低\n",
       "96       03-16        不限   3500          低\n",
       "113      03-16        本科   3500          低\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('salary', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 30.取出第33行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    03-16\n",
       "education        硕士\n",
       "salary        22500\n",
       "categories        高\n",
       "Name: 32, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31.计算salary列的中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17500.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(df['salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32.绘制薪资水平频率分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9tKwke5Lc0O2+EdiH/bTU5cDNSQ4CvwD8KvYR4Bz9pN3HsU8I1ykca8FNjBa5uyXJLcBfAu+yj44xB+xP8l7gGUZ/lx62n46qqh1Htruw/zX8eQN8YGrilntC+FSOtcg+6sd+OjH7aMSgl6TGOUcvSY0z6CWpcQa9JDXOoJekxhn0ktS4/wdqqGbfQkI7jgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#执行两次\n",
    "df.salary.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 33.绘制薪资水平密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.salary.plot(kind='kde',xlim=(0,80000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 34.删除最后一列categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary\n",
       "0        03-16        本科  27500\n",
       "1        03-16        本科  30000\n",
       "2        03-16        不限  27500\n",
       "3        03-16        本科  16500\n",
       "4        03-16        本科  15000\n",
       "..         ...       ...    ...\n",
       "130      03-16        本科  14000\n",
       "131      03-16        硕士  37500\n",
       "132      03-16        本科  30000\n",
       "133      03-16        本科  19000\n",
       "134      03-16        本科  30000\n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['categories']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 35.将df的第一列与第二列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test\n",
       "0        03-16        本科  27500  本科03-16\n",
       "1        03-16        本科  30000  本科03-16\n",
       "2        03-16        不限  27500  不限03-16\n",
       "3        03-16        本科  16500  本科03-16\n",
       "4        03-16        本科  15000  本科03-16\n",
       "..         ...       ...    ...      ...\n",
       "130      03-16        本科  14000  本科03-16\n",
       "131      03-16        硕士  37500  硕士03-16\n",
       "132      03-16        本科  30000  本科03-16\n",
       "133      03-16        本科  19000  本科03-16\n",
       "134      03-16        本科  30000  本科03-16\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['test'] = df['education']+df['createTime']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 36.将education列与salary列合并为新的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科\n",
       "..         ...       ...    ...      ...        ...\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科\n",
       "\n",
       "[135 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注：salary为int类型，操作与35题有所不同\n",
    "df[\"test1\"] = df[\"salary\"].map(str) + df['education']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 37.计算salary最大值与最小值之差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary    41500.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['salary']].apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 38.将第一行与最后一行拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df[:1], df[-2:-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 39.将第8行数据添加至末尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>12500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>12500.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科\n",
       "..         ...       ...    ...      ...        ...\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科\n",
       "7        03-16        本科  12500  本科03-16  12500.0本科\n",
       "\n",
       "[136 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(df.iloc[7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 40.查看每列的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "createTime    object\n",
       "education     object\n",
       "salary        object\n",
       "test          object\n",
       "test1         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 41.将createTime列设置为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createTime</th>\n",
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       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
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       "      <td>27500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
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       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-16</th>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           education salary     test      test1\n",
       "createTime                                     \n",
       "03-16             本科  27500  本科03-16  27500.0本科\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "03-16             不限  27500  不限03-16  27500.0不限\n",
       "03-16             本科  16500  本科03-16  16500.0本科\n",
       "03-16             本科  15000  本科03-16  15000.0本科\n",
       "...              ...    ...      ...        ...\n",
       "03-16             本科  14000  本科03-16  14000.0本科\n",
       "03-16             硕士  37500  硕士03-16  37500.0硕士\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "03-16             本科  19000  本科03-16  19000.0本科\n",
       "03-16             本科  30000  本科03-16  30000.0本科\n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"createTime\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 42.生成一个和df长度相同的随机数dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0    6\n",
       "1    8\n",
       "2    6\n",
       "3    3\n",
       "4    5\n",
       "..  ..\n",
       "130  9\n",
       "131  3\n",
       "132  9\n",
       "133  8\n",
       "134  3\n",
       "\n",
       "[135 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(pd.Series(np.random.randint(1, 10, 135)))\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 43.将上一题生成的dataframe与df合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科  6\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科  8\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限  6\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科  3\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科  5\n",
       "..         ...       ...    ...      ...        ... ..\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科  9\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  3\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科  9\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科  8\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科  3\n",
       "\n",
       "[135 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.concat([df,df1],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 44.生成新的一列new为salary列减去之前生成随机数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>27500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>27500.0本科</td>\n",
       "      <td>6</td>\n",
       "      <td>27494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>8</td>\n",
       "      <td>29992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-16</td>\n",
       "      <td>不限</td>\n",
       "      <td>27500</td>\n",
       "      <td>不限03-16</td>\n",
       "      <td>27500.0不限</td>\n",
       "      <td>6</td>\n",
       "      <td>27494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>16500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>16500.0本科</td>\n",
       "      <td>3</td>\n",
       "      <td>16497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>15000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>15000.0本科</td>\n",
       "      <td>5</td>\n",
       "      <td>14995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>14000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>14000.0本科</td>\n",
       "      <td>9</td>\n",
       "      <td>13991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>3</td>\n",
       "      <td>37497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>9</td>\n",
       "      <td>29991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>19000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>19000.0本科</td>\n",
       "      <td>8</td>\n",
       "      <td>18992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>30000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>30000.0本科</td>\n",
       "      <td>3</td>\n",
       "      <td>29997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0    new\n",
       "0        03-16        本科  27500  本科03-16  27500.0本科  6  27494\n",
       "1        03-16        本科  30000  本科03-16  30000.0本科  8  29992\n",
       "2        03-16        不限  27500  不限03-16  27500.0不限  6  27494\n",
       "3        03-16        本科  16500  本科03-16  16500.0本科  3  16497\n",
       "4        03-16        本科  15000  本科03-16  15000.0本科  5  14995\n",
       "..         ...       ...    ...      ...        ... ..    ...\n",
       "130      03-16        本科  14000  本科03-16  14000.0本科  9  13991\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  3  37497\n",
       "132      03-16        本科  30000  本科03-16  30000.0本科  9  29991\n",
       "133      03-16        本科  19000  本科03-16  19000.0本科  8  18992\n",
       "134      03-16        本科  30000  本科03-16  30000.0本科  3  29997\n",
       "\n",
       "[135 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"new\"] = df[\"salary\"] - df[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 45.检查数据中是否含有任何缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 46.将salary列类型转换为浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      27500.0\n",
       "1      30000.0\n",
       "2      27500.0\n",
       "3      16500.0\n",
       "4      15000.0\n",
       "        ...   \n",
       "130    14000.0\n",
       "131    37500.0\n",
       "132    30000.0\n",
       "133    19000.0\n",
       "134    30000.0\n",
       "Name: salary, Length: 135, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salary'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 47.计算salary大于10000的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['salary']>10000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 48.查看每种学历出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    119\n",
       "硕士      7\n",
       "不限      5\n",
       "大专      4\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 49.查看education列共有几种学历"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 50.提取salary与new列的和大于60000的最后3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>education</th>\n",
       "      <th>salary</th>\n",
       "      <th>test</th>\n",
       "      <th>test1</th>\n",
       "      <th>0</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>35000</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>35000.0本科</td>\n",
       "      <td>4</td>\n",
       "      <td>34996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>03-16</td>\n",
       "      <td>本科</td>\n",
       "      <td>37500</td>\n",
       "      <td>本科03-16</td>\n",
       "      <td>37500.0本科</td>\n",
       "      <td>1</td>\n",
       "      <td>37499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>03-16</td>\n",
       "      <td>硕士</td>\n",
       "      <td>37500</td>\n",
       "      <td>硕士03-16</td>\n",
       "      <td>37500.0硕士</td>\n",
       "      <td>3</td>\n",
       "      <td>37497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    createTime education salary     test      test1  0    new\n",
       "92       03-16        本科  35000  本科03-16  35000.0本科  4  34996\n",
       "101      03-16        本科  37500  本科03-16  37500.0本科  1  37499\n",
       "131      03-16        硕士  37500  硕士03-16  37500.0硕士  3  37497"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[['salary','new']]\n",
    "rowsums = df1.apply(np.sum, axis=1)\n",
    "res = df.iloc[np.where(rowsums > 60000)[0][-3:], :]\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三期 金融数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 51.使用绝对路径读取本地Excel数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    }
   ],
   "source": [
    "#请将下面的路径替换为你存储数据的路径\n",
    "data = pd.read_excel('600000.SH.xls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 52.查看数据前三行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>16.1356</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>16.1444</td>\n",
       "      <td>15.4997</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>42240610</td>\n",
       "      <td>754425783</td>\n",
       "      <td>-0.4151</td>\n",
       "      <td>-2.5725</td>\n",
       "      <td>17.8602</td>\n",
       "      <td>0.2264</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>3.320318e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.5614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>15.4644</td>\n",
       "      <td>15.9501</td>\n",
       "      <td>15.3672</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>58054793</td>\n",
       "      <td>1034181474</td>\n",
       "      <td>0.1413</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>17.8139</td>\n",
       "      <td>0.3112</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>3.350163e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>15.8618</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>16.0208</td>\n",
       "      <td>15.6234</td>\n",
       "      <td>15.9855</td>\n",
       "      <td>46772653</td>\n",
       "      <td>838667398</td>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.7795</td>\n",
       "      <td>17.9307</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>3.376278e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "0  600000.SH  浦发银行 2016-01-04  16.1356  16.1444  16.1444  15.4997  15.7205   \n",
       "1  600000.SH  浦发银行 2016-01-05  15.7205  15.4644  15.9501  15.3672  15.8618   \n",
       "2  600000.SH  浦发银行 2016-01-06  15.8618  15.8088  16.0208  15.6234  15.9855   \n",
       "\n",
       "     成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "0  42240610   754425783 -0.4151 -2.5725  17.8602  0.2264  3.320318e+11   \n",
       "1  58054793  1034181474  0.1413  0.8989  17.8139  0.3112  3.350163e+11   \n",
       "2  46772653   838667398  0.1236  0.7795  17.9307  0.2507  3.376278e+11   \n",
       "\n",
       "         总市值(元)     A股流通股本(股)     市盈率  \n",
       "0  3.320318e+11  1.865347e+10  6.5614  \n",
       "1  3.350163e+11  1.865347e+10  6.6204  \n",
       "2  3.376278e+11  1.865347e+10  6.6720  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 53.查看每列数据缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "代码           1\n",
       "简称           2\n",
       "日期           2\n",
       "前收盘价(元)      2\n",
       "开盘价(元)       2\n",
       "最高价(元)       2\n",
       "最低价(元)       2\n",
       "收盘价(元)       2\n",
       "成交量(股)       2\n",
       "成交金额(元)      2\n",
       "涨跌(元)        2\n",
       "涨跌幅(%)       2\n",
       "均价(元)        2\n",
       "换手率(%)       2\n",
       "A股流通市值(元)    2\n",
       "总市值(元)       2\n",
       "A股流通股本(股)    2\n",
       "市盈率          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 54.提取日期列含有空值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>数据来源：Wind资讯</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              代码   简称  日期  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元) 成交量(股)  \\\n",
       "327          NaN  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "328  数据来源：Wind资讯  NaN NaT      NaN     NaN     NaN     NaN     NaN    NaN   \n",
       "\n",
       "    成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)  A股流通市值(元)  总市值(元)  A股流通股本(股)  市盈率  \n",
       "327     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  \n",
       "328     NaN    NaN     NaN   NaN    NaN        NaN     NaN        NaN  NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['日期'].isnull()]  # isnull和isna一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 55.输出每列缺失值具体行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列名：\"代码\", 第[327]行位置有缺失值\n",
      "列名：\"简称\", 第[327, 328]行位置有缺失值\n",
      "列名：\"日期\", 第[327, 328]行位置有缺失值\n",
      "列名：\"前收盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"开盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"最高价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"最低价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"收盘价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"成交量(股)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"成交金额(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"涨跌(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"涨跌幅(%)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"均价(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"换手率(%)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"A股流通市值(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"总市值(元)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"A股流通股本(股)\", 第[327, 328]行位置有缺失值\n",
      "列名：\"市盈率\", 第[327, 328]行位置有缺失值\n"
     ]
    }
   ],
   "source": [
    "for columname in data.columns:\n",
    "    if data[columname].count() != len(data):\n",
    "        loc = data[columname][data[columname].isnull().values==True].index.tolist()\n",
    "        print('列名：\"{}\", 第{}行位置有缺失值'.format(columname,loc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 56.删除所有存在缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "备注\n",
    "axis：0-行操作（默认），1-列操作\n",
    "how：any-只要有空值就删除（默认），all-全部为空值才删除\n",
    "inplace：False-返回新的数据集（默认），True-在原数据集上操作\n",
    "'''\n",
    "data.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 57.绘制收盘价的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('seaborn-darkgrid') # 设置画图的风格\n",
    "plt.rc('font',  size=6) #设置图中字体和大小\n",
    "plt.rc('figure', figsize=(4,3), dpi=150) # 设置图的大小\n",
    "data['收盘价(元)'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 58.同时绘制开盘价与收盘价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['收盘价(元)','开盘价(元)']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 59.绘制涨跌幅的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['涨跌幅(%)'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 60.让直方图更细致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['涨跌幅(%)'].hist(bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 61.以data的列名创建一个dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame(columns = data.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 62.打印所有换手率不是数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(data)):\n",
    "    if type(data.iloc[i,13]) != float:\n",
    "        temp = temp.append(data.loc[i])\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 63.打印所有换手率为--的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>日期</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-16</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-17</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-18</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-22</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>16.2946</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>3.441565e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           代码    简称         日期  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)   收盘价(元)  \\\n",
       "26  600000.SH  浦发银行 2016-02-16  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "27  600000.SH  浦发银行 2016-02-17  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "28  600000.SH  浦发银行 2016-02-18  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "29  600000.SH  浦发银行 2016-02-19  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "30  600000.SH  浦发银行 2016-02-22  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "31  600000.SH  浦发银行 2016-02-23  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "32  600000.SH  浦发银行 2016-02-24  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "33  600000.SH  浦发银行 2016-02-25  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "34  600000.SH  浦发银行 2016-02-26  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "35  600000.SH  浦发银行 2016-02-29  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "36  600000.SH  浦发银行 2016-03-01  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "37  600000.SH  浦发银行 2016-03-02  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "38  600000.SH  浦发银行 2016-03-03  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "39  600000.SH  浦发银行 2016-03-04  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "40  600000.SH  浦发银行 2016-03-07  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "41  600000.SH  浦发银行 2016-03-08  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "42  600000.SH  浦发银行 2016-03-09  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "43  600000.SH  浦发银行 2016-03-10  16.2946  16.2946  16.2946  16.2946  16.2946   \n",
       "\n",
       "   成交量(股) 成交金额(元)  涨跌(元)  涨跌幅(%) 均价(元) 换手率(%)     A股流通市值(元)        总市值(元)  \\\n",
       "26     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "27     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "28     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "29     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "30     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "31     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "32     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "33     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "34     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "35     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "36     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "37     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "38     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "39     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "40     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "41     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "42     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "43     --      --    0.0     0.0    --     --  3.441565e+11  3.441565e+11   \n",
       "\n",
       "       A股流通股本(股)    市盈率  \n",
       "26  1.865347e+10  6.801  \n",
       "27  1.865347e+10  6.801  \n",
       "28  1.865347e+10  6.801  \n",
       "29  1.865347e+10  6.801  \n",
       "30  1.865347e+10  6.801  \n",
       "31  1.865347e+10  6.801  \n",
       "32  1.865347e+10  6.801  \n",
       "33  1.865347e+10  6.801  \n",
       "34  1.865347e+10  6.801  \n",
       "35  1.865347e+10  6.801  \n",
       "36  1.865347e+10  6.801  \n",
       "37  1.865347e+10  6.801  \n",
       "38  1.865347e+10  6.801  \n",
       "39  1.865347e+10  6.801  \n",
       "40  1.865347e+10  6.801  \n",
       "41  1.865347e+10  6.801  \n",
       "42  1.865347e+10  6.801  \n",
       "43  1.865347e+10  6.801  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['换手率(%)'].isin(['--'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 64.重置data的行号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 65.删除所有换手率为非数字的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "k =[]\n",
    "for i in range(len(data)):\n",
    "    if type(data.iloc[i,13]) != float:\n",
    "        k.append(i)\n",
    "data.drop(labels=k,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 66.绘制换手率的密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['换手率(%)'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 67.计算前一天与后一天收盘价的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1      0.1413\n",
       "2      0.1237\n",
       "3     -0.5211\n",
       "4     -0.0177\n",
       "        ...  \n",
       "322   -0.0800\n",
       "323   -0.1000\n",
       "324   -0.0600\n",
       "325   -0.0600\n",
       "326   -0.1000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['收盘价(元)'].diff()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 68.计算前一天与后一天收盘价变化率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1      0.008988\n",
       "2      0.007799\n",
       "3     -0.032598\n",
       "4     -0.001145\n",
       "         ...   \n",
       "322   -0.005277\n",
       "323   -0.006631\n",
       "324   -0.004005\n",
       "325   -0.004021\n",
       "326   -0.006729\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['收盘价(元)'].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 69.设置日期为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.set_index('日期')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 70.以5个数据作为一个数据滑动窗口，在这个5个数据上取均值(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04         NaN\n",
       "2016-01-05         NaN\n",
       "2016-01-06         NaN\n",
       "2016-01-07         NaN\n",
       "2016-01-08    15.69578\n",
       "                ...   \n",
       "2017-05-03    15.14200\n",
       "2017-05-04    15.12800\n",
       "2017-05-05    15.07000\n",
       "2017-05-08    15.00000\n",
       "2017-05-09    14.92000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['收盘价(元)'].rolling(5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 71.以5个数据作为一个数据滑动窗口，计算这五个数据总和(收盘价)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04        NaN\n",
       "2016-01-05        NaN\n",
       "2016-01-06        NaN\n",
       "2016-01-07        NaN\n",
       "2016-01-08    78.4789\n",
       "               ...   \n",
       "2017-05-03    75.7100\n",
       "2017-05-04    75.6400\n",
       "2017-05-05    75.3500\n",
       "2017-05-08    75.0000\n",
       "2017-05-09    74.6000\n",
       "Name: 收盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['收盘价(元)'].rolling(5).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 72.将收盘价5日均线、20日均线与原始数据绘制在同一个图上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='日期'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['收盘价(元)'].plot()\n",
    "data['收盘价(元)'].rolling(5).mean().plot()\n",
    "data['收盘价(元)'].rolling(20).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 73.按周为采样规则，取一周收盘价最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-10    15.9855\n",
       "2016-01-17    15.8265\n",
       "2016-01-24    15.6940\n",
       "2016-01-31    15.0405\n",
       "2016-02-07    16.2328\n",
       "               ...   \n",
       "2017-04-16    15.9700\n",
       "2017-04-23    15.5600\n",
       "2017-04-30    15.2100\n",
       "2017-05-07    15.1600\n",
       "2017-05-14    14.8600\n",
       "Freq: W-SUN, Name: 收盘价(元), Length: 71, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['收盘价(元)'].resample('W').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 74.绘制重采样数据与原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='日期'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['收盘价(元)'].plot()\n",
    "data['收盘价(元)'].resample('7D').max().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 75.将数据往后移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
       "      <th>最高价(元)</th>\n",
       "      <th>最低价(元)</th>\n",
       "      <th>收盘价(元)</th>\n",
       "      <th>成交量(股)</th>\n",
       "      <th>成交金额(元)</th>\n",
       "      <th>涨跌(元)</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>均价(元)</th>\n",
       "      <th>换手率(%)</th>\n",
       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
       "      <th>A股流通股本(股)</th>\n",
       "      <th>市盈率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>317.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.02</td>\n",
       "      <td>15.10</td>\n",
       "      <td>14.99</td>\n",
       "      <td>15.05</td>\n",
       "      <td>12975919</td>\n",
       "      <td>195296862</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>15.0507</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>318.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.06</td>\n",
       "      <td>15.11</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.05</td>\n",
       "      <td>14939871</td>\n",
       "      <td>225022668</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.0619</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>3.253551e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>319.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.25</td>\n",
       "      <td>15.03</td>\n",
       "      <td>15.21</td>\n",
       "      <td>22887645</td>\n",
       "      <td>345791526</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0631</td>\n",
       "      <td>15.1082</td>\n",
       "      <td>0.1059</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>320.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.15</td>\n",
       "      <td>15.22</td>\n",
       "      <td>15.08</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15718509</td>\n",
       "      <td>238419161</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.1681</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>321.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.22</td>\n",
       "      <td>15.13</td>\n",
       "      <td>15.16</td>\n",
       "      <td>12607509</td>\n",
       "      <td>191225527</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.3287</td>\n",
       "      <td>15.1676</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>3.277331e+11</td>\n",
       "      <td>3.277331e+11</td>\n",
       "      <td>2.161828e+10</td>\n",
       "      <td>6.1721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index         代码    简称  前收盘价(元)  开盘价(元)  最高价(元)  最低价(元)  收盘价(元)  \\\n",
       "日期                                                                            \n",
       "2016-01-04    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-05    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-06    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-07    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "2016-01-08    NaN        NaN   NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "...           ...        ...   ...      ...     ...     ...     ...     ...   \n",
       "2017-05-03  317.0  600000.SH  浦发银行    15.00   15.02   15.10   14.99   15.05   \n",
       "2017-05-04  318.0  600000.SH  浦发银行    15.05   15.06   15.11   15.00   15.05   \n",
       "2017-05-05  319.0  600000.SH  浦发银行    15.05   15.05   15.25   15.03   15.21   \n",
       "2017-05-08  320.0  600000.SH  浦发银行    15.21   15.15   15.22   15.08   15.21   \n",
       "2017-05-09  321.0  600000.SH  浦发银行    15.21   15.21   15.22   15.13   15.16   \n",
       "\n",
       "              成交量(股)    成交金额(元)  涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)     A股流通市值(元)  \\\n",
       "日期                                                                              \n",
       "2016-01-04       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-05       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-06       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-07       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "2016-01-08       NaN        NaN    NaN     NaN      NaN     NaN           NaN   \n",
       "...              ...        ...    ...     ...      ...     ...           ...   \n",
       "2017-05-03  12975919  195296862   0.05  0.3333  15.0507    0.06  3.253551e+11   \n",
       "2017-05-04  14939871  225022668   0.00  0.0000  15.0619  0.0691  3.253551e+11   \n",
       "2017-05-05  22887645  345791526   0.16  1.0631  15.1082  0.1059  3.288140e+11   \n",
       "2017-05-08  15718509  238419161   0.00  0.0000  15.1681  0.0727  3.288140e+11   \n",
       "2017-05-09  12607509  191225527  -0.05 -0.3287  15.1676  0.0583  3.277331e+11   \n",
       "\n",
       "                  总市值(元)     A股流通股本(股)     市盈率  \n",
       "日期                                              \n",
       "2016-01-04           NaN           NaN     NaN  \n",
       "2016-01-05           NaN           NaN     NaN  \n",
       "2016-01-06           NaN           NaN     NaN  \n",
       "2016-01-07           NaN           NaN     NaN  \n",
       "2016-01-08           NaN           NaN     NaN  \n",
       "...                  ...           ...     ...  \n",
       "2017-05-03  3.253551e+11  2.161828e+10  6.1273  \n",
       "2017-05-04  3.253551e+11  2.161828e+10  6.1273  \n",
       "2017-05-05  3.288140e+11  2.161828e+10  6.1925  \n",
       "2017-05-08  3.288140e+11  2.161828e+10  6.1925  \n",
       "2017-05-09  3.277331e+11  2.161828e+10  6.1721  \n",
       "\n",
       "[309 rows x 18 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shift(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 76.将数据向前移动5天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>代码</th>\n",
       "      <th>简称</th>\n",
       "      <th>前收盘价(元)</th>\n",
       "      <th>开盘价(元)</th>\n",
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       "      <th>A股流通市值(元)</th>\n",
       "      <th>总市值(元)</th>\n",
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       "      <th>市盈率</th>\n",
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       "      <th>日期</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.4467</td>\n",
       "      <td>15.1994</td>\n",
       "      <td>15.4114</td>\n",
       "      <td>14.9786</td>\n",
       "      <td>15.0581</td>\n",
       "      <td>90177135</td>\n",
       "      <td>1550155933</td>\n",
       "      <td>-0.3886</td>\n",
       "      <td>-2.5157</td>\n",
       "      <td>17.1901</td>\n",
       "      <td>0.4834</td>\n",
       "      <td>3.180417e+11</td>\n",
       "      <td>3.180417e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.2849</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>6.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.0581</td>\n",
       "      <td>15.1641</td>\n",
       "      <td>15.4732</td>\n",
       "      <td>15.0846</td>\n",
       "      <td>15.4114</td>\n",
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       "      <td>0.3533</td>\n",
       "      <td>2.3460</td>\n",
       "      <td>17.4099</td>\n",
       "      <td>0.2969</td>\n",
       "      <td>3.255031e+11</td>\n",
       "      <td>3.255031e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>7.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.4114</td>\n",
       "      <td>15.5174</td>\n",
       "      <td>15.8088</td>\n",
       "      <td>15.3231</td>\n",
       "      <td>15.3584</td>\n",
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       "      <td>843717365</td>\n",
       "      <td>-0.0530</td>\n",
       "      <td>-0.3438</td>\n",
       "      <td>17.6254</td>\n",
       "      <td>0.2566</td>\n",
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       "      <td>3.243839e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4102</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>8.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.3584</td>\n",
       "      <td>15.0140</td>\n",
       "      <td>15.8883</td>\n",
       "      <td>14.9168</td>\n",
       "      <td>15.8265</td>\n",
       "      <td>54838833</td>\n",
       "      <td>966117848</td>\n",
       "      <td>0.4681</td>\n",
       "      <td>3.0477</td>\n",
       "      <td>17.6174</td>\n",
       "      <td>0.294</td>\n",
       "      <td>3.342702e+11</td>\n",
       "      <td>3.342702e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.6056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>9.0</td>\n",
       "      <td>600000.SH</td>\n",
       "      <td>浦发银行</td>\n",
       "      <td>15.8265</td>\n",
       "      <td>15.7205</td>\n",
       "      <td>16.0296</td>\n",
       "      <td>15.4732</td>\n",
       "      <td>15.5262</td>\n",
       "      <td>46723139</td>\n",
       "      <td>836146426</td>\n",
       "      <td>-0.3003</td>\n",
       "      <td>-1.8973</td>\n",
       "      <td>17.8958</td>\n",
       "      <td>0.2505</td>\n",
       "      <td>3.279280e+11</td>\n",
       "      <td>3.279280e+11</td>\n",
       "      <td>1.865347e+10</td>\n",
       "      <td>6.4803</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index         代码    简称  前收盘价(元)   开盘价(元)   最高价(元)   最低价(元)  \\\n",
       "日期                                                                       \n",
       "2016-01-04    5.0  600000.SH  浦发银行  15.4467  15.1994  15.4114  14.9786   \n",
       "2016-01-05    6.0  600000.SH  浦发银行  15.0581  15.1641  15.4732  15.0846   \n",
       "2016-01-06    7.0  600000.SH  浦发银行  15.4114  15.5174  15.8088  15.3231   \n",
       "2016-01-07    8.0  600000.SH  浦发银行  15.3584  15.0140  15.8883  14.9168   \n",
       "2016-01-08    9.0  600000.SH  浦发银行  15.8265  15.7205  16.0296  15.4732   \n",
       "...           ...        ...   ...      ...      ...      ...      ...   \n",
       "2017-05-03    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-04    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-05    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-08    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "2017-05-09    NaN        NaN   NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "             收盘价(元)    成交量(股)     成交金额(元)   涨跌(元)  涨跌幅(%)    均价(元)  换手率(%)  \\\n",
       "日期                                                                           \n",
       "2016-01-04  15.0581  90177135  1550155933 -0.3886 -2.5157  17.1901  0.4834   \n",
       "2016-01-05  15.4114  55374454   964061502  0.3533  2.3460  17.4099  0.2969   \n",
       "2016-01-06  15.3584  47869312   843717365 -0.0530 -0.3438  17.6254  0.2566   \n",
       "2016-01-07  15.8265  54838833   966117848  0.4681  3.0477  17.6174   0.294   \n",
       "2016-01-08  15.5262  46723139   836146426 -0.3003 -1.8973  17.8958  0.2505   \n",
       "...             ...       ...         ...     ...     ...      ...     ...   \n",
       "2017-05-03      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-04      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-05      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-08      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "2017-05-09      NaN       NaN         NaN     NaN     NaN      NaN     NaN   \n",
       "\n",
       "               A股流通市值(元)        总市值(元)     A股流通股本(股)     市盈率  \n",
       "日期                                                            \n",
       "2016-01-04  3.180417e+11  3.180417e+11  1.865347e+10  6.2849  \n",
       "2016-01-05  3.255031e+11  3.255031e+11  1.865347e+10  6.4324  \n",
       "2016-01-06  3.243839e+11  3.243839e+11  1.865347e+10  6.4102  \n",
       "2016-01-07  3.342702e+11  3.342702e+11  1.865347e+10  6.6056  \n",
       "2016-01-08  3.279280e+11  3.279280e+11  1.865347e+10  6.4803  \n",
       "...                  ...           ...           ...     ...  \n",
       "2017-05-03           NaN           NaN           NaN     NaN  \n",
       "2017-05-04           NaN           NaN           NaN     NaN  \n",
       "2017-05-05           NaN           NaN           NaN     NaN  \n",
       "2017-05-08           NaN           NaN           NaN     NaN  \n",
       "2017-05-09           NaN           NaN           NaN     NaN  \n",
       "\n",
       "[309 rows x 18 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shift(-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 77.使用expending函数计算开盘价的移动窗口均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "日期\n",
       "2016-01-04    16.144400\n",
       "2016-01-05    15.804400\n",
       "2016-01-06    15.805867\n",
       "2016-01-07    15.784525\n",
       "2016-01-08    15.761120\n",
       "                ...    \n",
       "2017-05-03    16.041489\n",
       "2017-05-04    16.038314\n",
       "2017-05-05    16.034769\n",
       "2017-05-08    16.030695\n",
       "2017-05-09    16.026356\n",
       "Name: 开盘价(元), Length: 309, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['开盘价(元)'].expanding(min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 78.绘制上一题的移动均值与原始数据折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='日期'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['expanding Open mean']=data['开盘价(元)'].expanding(min_periods=1).mean()\n",
    "data[['开盘价(元)', 'expanding Open mean']].plot(figsize=(16, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 79.计算布林指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['former 30 days rolling Close mean']=data['收盘价(元)'].rolling(20).mean()\n",
    "data['upper bound']=data['former 30 days rolling Close mean']+2*data['收盘价(元)'].rolling(20).std()#在这里我们取20天内的标准差\n",
    "data['lower bound']=data['former 30 days rolling Close mean']-2*data['收盘价(元)'].rolling(20).std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80.计算布林线并绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='日期'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 25910 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30424 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "f:\\virtual_environment\\ai\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['收盘价(元)', 'former 30 days rolling Close mean','upper bound','lower bound' ]].plot(figsize=(16, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四期 当Pandas遇上NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 81.导入并查看pandas与numpy版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.1\n",
      "1.1.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "print(np.__version__)\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 82.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <td>64</td>\n",
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       "      <td>16</td>\n",
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       "      <th>16</th>\n",
       "      <td>72</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
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      "text/plain": [
       "     0\n",
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       "5   99\n",
       "6    6\n",
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       "8   37\n",
       "9   44\n",
       "10  64\n",
       "11  16\n",
       "12  88\n",
       "13  35\n",
       "14  59\n",
       "15  36\n",
       "16  72\n",
       "17  32\n",
       "18  57\n",
       "19  71"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 使用numpy生成20个0-100随机数\n",
    "tem = np.random.randint(1,100,20)\n",
    "df1 = pd.DataFrame(tem)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 83.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <th>16</th>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
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       "      <th>18</th>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>95</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0    0\n",
       "1    5\n",
       "2   10\n",
       "3   15\n",
       "4   20\n",
       "5   25\n",
       "6   30\n",
       "7   35\n",
       "8   40\n",
       "9   45\n",
       "10  50\n",
       "11  55\n",
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       "14  70\n",
       "15  75\n",
       "16  80\n",
       "17  85\n",
       "18  90\n",
       "19  95"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 使用numpy生成20个0-100固定步长的数\n",
    "tem = np.arange(0,100,5)\n",
    "df2 = pd.DataFrame(tem)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 84.从NumPy数组创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>0.713844</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.638720</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.281126</td>\n",
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       "      <th>5</th>\n",
       "      <td>-0.124151</td>\n",
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       "      <th>6</th>\n",
       "      <td>-0.856257</td>\n",
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       "      <th>7</th>\n",
       "      <td>1.358299</td>\n",
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       "      <td>0.916383</td>\n",
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       "      <th>9</th>\n",
       "      <td>1.009796</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.238031</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.934393</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.619941</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.123291</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.793956</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-2.432179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.463813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.013763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.354148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.480061</td>\n",
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      ],
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       "           0\n",
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       "1   0.713844\n",
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       "4   1.281126\n",
       "5  -0.124151\n",
       "6  -0.856257\n",
       "7   1.358299\n",
       "8   0.916383\n",
       "9   1.009796\n",
       "10  1.238031\n",
       "11 -0.934393\n",
       "12 -0.619941\n",
       "13 -0.123291\n",
       "14  0.793956\n",
       "15 -2.432179\n",
       "16  0.463813\n",
       "17  0.013763\n",
       "18 -0.354148\n",
       "19  0.480061"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 使用numpy生成20个指定分布(如标准正态分布)的数\n",
    "tem = np.random.normal(0, 1, 20)\n",
    "df3 = pd.DataFrame(tem)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 85.将df1，df2，df3按照行合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>4.000000</td>\n",
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       "      <td>56.000000</td>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
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       "      <th>9</th>\n",
       "      <td>44.000000</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64.000000</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>88.000000</td>\n",
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       "      <th>13</th>\n",
       "      <td>35.000000</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>59.000000</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-1.702005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.713844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-0.812883</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.638720</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.281126</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-0.124151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-0.856257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.358299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.916383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1.009796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1.238031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>-0.934393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>-0.619941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>-0.123291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.793956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>-2.432179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.463813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.013763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-0.354148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.480061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "0   28.000000\n",
       "1   19.000000\n",
       "2   89.000000\n",
       "3    4.000000\n",
       "4   56.000000\n",
       "5   99.000000\n",
       "6    6.000000\n",
       "7   43.000000\n",
       "8   37.000000\n",
       "9   44.000000\n",
       "10  64.000000\n",
       "11  16.000000\n",
       "12  88.000000\n",
       "13  35.000000\n",
       "14  59.000000\n",
       "15  36.000000\n",
       "16  72.000000\n",
       "17  32.000000\n",
       "18  57.000000\n",
       "19  71.000000\n",
       "20   0.000000\n",
       "21   5.000000\n",
       "22  10.000000\n",
       "23  15.000000\n",
       "24  20.000000\n",
       "25  25.000000\n",
       "26  30.000000\n",
       "27  35.000000\n",
       "28  40.000000\n",
       "29  45.000000\n",
       "30  50.000000\n",
       "31  55.000000\n",
       "32  60.000000\n",
       "33  65.000000\n",
       "34  70.000000\n",
       "35  75.000000\n",
       "36  80.000000\n",
       "37  85.000000\n",
       "38  90.000000\n",
       "39  95.000000\n",
       "40  -1.702005\n",
       "41   0.713844\n",
       "42  -0.812883\n",
       "43   0.638720\n",
       "44   1.281126\n",
       "45  -0.124151\n",
       "46  -0.856257\n",
       "47   1.358299\n",
       "48   0.916383\n",
       "49   1.009796\n",
       "50   1.238031\n",
       "51  -0.934393\n",
       "52  -0.619941\n",
       "53  -0.123291\n",
       "54   0.793956\n",
       "55  -2.432179\n",
       "56   0.463813\n",
       "57   0.013763\n",
       "58  -0.354148\n",
       "59   0.480061"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df1,df2,df3],axis=0,ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 86.将df1，df2，df3按照列合并为新DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.702005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0.713844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.812883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.638720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>1.281126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99</td>\n",
       "      <td>25</td>\n",
       "      <td>-0.124151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.856257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>1.358299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>0.916383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>1.009796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64</td>\n",
       "      <td>50</td>\n",
       "      <td>1.238031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>-0.934393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>88</td>\n",
       "      <td>60</td>\n",
       "      <td>-0.619941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "      <td>-0.123291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>0.793956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36</td>\n",
       "      <td>75</td>\n",
       "      <td>-2.432179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>0.463813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>85</td>\n",
       "      <td>0.013763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57</td>\n",
       "      <td>90</td>\n",
       "      <td>-0.354148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>71</td>\n",
       "      <td>95</td>\n",
       "      <td>0.480061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1         2\n",
       "0   28   0 -1.702005\n",
       "1   19   5  0.713844\n",
       "2   89  10 -0.812883\n",
       "3    4  15  0.638720\n",
       "4   56  20  1.281126\n",
       "5   99  25 -0.124151\n",
       "6    6  30 -0.856257\n",
       "7   43  35  1.358299\n",
       "8   37  40  0.916383\n",
       "9   44  45  1.009796\n",
       "10  64  50  1.238031\n",
       "11  16  55 -0.934393\n",
       "12  88  60 -0.619941\n",
       "13  35  65 -0.123291\n",
       "14  59  70  0.793956\n",
       "15  36  75 -2.432179\n",
       "16  72  80  0.463813\n",
       "17  32  85  0.013763\n",
       "18  57  90 -0.354148\n",
       "19  71  95  0.480061"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df1,df2,df3],axis=1,ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 87.查看df所有数据的最小值、25%分位数、中位数、75%分位数、最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.43217929  0.77392795 26.5        57.5        99.        ]\n"
     ]
    }
   ],
   "source": [
    "print(np.percentile(df, q=[0, 25, 50, 75, 100]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 88.修改列名为col1,col2,col3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['col1','col2','col3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 89.提取第一列中不在第二列出现的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     28\n",
       "1     19\n",
       "2     89\n",
       "3      4\n",
       "4     56\n",
       "5     99\n",
       "6      6\n",
       "7     43\n",
       "8     37\n",
       "9     44\n",
       "10    64\n",
       "11    16\n",
       "12    88\n",
       "14    59\n",
       "15    36\n",
       "16    72\n",
       "17    32\n",
       "18    57\n",
       "19    71\n",
       "Name: col1, dtype: int32"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'][~df['col1'].isin(df['col2'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 90.提取第一列和第二列出现频率最高的三个数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([35, 70, 80], dtype='int64')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df['col1'].append(df['col2'])\n",
    "temp.value_counts().index[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 91.计算第一列数字前一个与后一个的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan,\n",
       " -9.0,\n",
       " 70.0,\n",
       " -85.0,\n",
       " 52.0,\n",
       " 43.0,\n",
       " -93.0,\n",
       " 37.0,\n",
       " -6.0,\n",
       " 7.0,\n",
       " 20.0,\n",
       " -48.0,\n",
       " 72.0,\n",
       " -53.0,\n",
       " 24.0,\n",
       " -23.0,\n",
       " 36.0,\n",
       " -40.0,\n",
       " 25.0,\n",
       " 14.0]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].diff().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 92.将col1,col2,clo3三列顺序颠倒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col3</th>\n",
       "      <th>col2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.702005</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.713844</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.812883</td>\n",
       "      <td>10</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.638720</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.281126</td>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.124151</td>\n",
       "      <td>25</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.856257</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.358299</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.916383</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.009796</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.238031</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.934393</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.619941</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.123291</td>\n",
       "      <td>65</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.793956</td>\n",
       "      <td>70</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-2.432179</td>\n",
       "      <td>75</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.463813</td>\n",
       "      <td>80</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.013763</td>\n",
       "      <td>85</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.354148</td>\n",
       "      <td>90</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.480061</td>\n",
       "      <td>95</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        col3  col2  col1\n",
       "0  -1.702005     0    28\n",
       "1   0.713844     5    19\n",
       "2  -0.812883    10    89\n",
       "3   0.638720    15     4\n",
       "4   1.281126    20    56\n",
       "5  -0.124151    25    99\n",
       "6  -0.856257    30     6\n",
       "7   1.358299    35    43\n",
       "8   0.916383    40    37\n",
       "9   1.009796    45    44\n",
       "10  1.238031    50    64\n",
       "11 -0.934393    55    16\n",
       "12 -0.619941    60    88\n",
       "13 -0.123291    65    35\n",
       "14  0.793956    70    59\n",
       "15 -2.432179    75    36\n",
       "16  0.463813    80    72\n",
       "17  0.013763    85    32\n",
       "18 -0.354148    90    57\n",
       "19  0.480061    95    71"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, ::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 93.提取第一列位置在1,10,15的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     19\n",
       "10    64\n",
       "15    36\n",
       "Name: col1, dtype: int32"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].take([1,10,15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 94.查找第一列的局部最大值位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  7, 10, 12, 14, 16], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 即比它前一个与后一个数字的都大的数字\n",
    "tem = np.diff(np.sign(np.diff(df['col1'])))\n",
    "np.where(tem == -2)[0] + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 95. 按行计算df的每一行均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      8.765998\n",
       "1      8.237948\n",
       "2     32.729039\n",
       "3      6.546240\n",
       "4     25.760375\n",
       "5     41.291950\n",
       "6     11.714581\n",
       "7     26.452766\n",
       "8     25.972128\n",
       "9     30.003265\n",
       "10    38.412677\n",
       "11    23.355202\n",
       "12    49.126686\n",
       "13    33.292236\n",
       "14    43.264652\n",
       "15    36.189274\n",
       "16    50.821271\n",
       "17    39.004588\n",
       "18    48.881951\n",
       "19    55.493354\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['col1','col2','col3']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 96.对第二列计算移动平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65.,\n",
       "       70., 75., 80., 85., 90.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 每次移动三个位置，不可以使用自定义函数\n",
    "\n",
    "np.convolve(df['col2'], np.ones(3)/3, mode='valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 97.将数据按照第三列值的大小升序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(\"col3\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 98.将第一列大于50的数字修改为'高'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\virtual_environment\\ai\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df.col1[df['col1'] > 50]= '高'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 99.计算第二列与第三列之间的欧式距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248.44833164972363"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(df['col2']-df['col3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第五期 一些补充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 101.从CSV文件中读取指定数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionName</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据建模</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>数据建模工程师</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  positionName  salary\n",
       "0         数据分析   37500\n",
       "1         数据建模   15000\n",
       "2         数据分析    3500\n",
       "3         数据分析   45000\n",
       "4         数据分析   30000\n",
       "5         数据分析   50000\n",
       "6         数据分析   30000\n",
       "7      数据建模工程师   35000\n",
       "8       数据分析专家   60000\n",
       "9        数据分析师   40000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#备注 从数据1中的前10行中读取positionName, salary两列\n",
    "\n",
    "df = pd.read_csv('数据1.csv',encoding='gbk', usecols=['positionName', 'salary'],nrows = 10)\n",
    "df"
   ]
  }
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